Abstract
In this study, we have proposed a model called breast blood perfusion (BBP) model to transform the raw grayscale breast thermogram to blood perfusion image. Typically, it is observed that the original grayscale thermal breast image (TBI) often suffers from the within-class scatter problem, which makes it very difficult to recognize the different temperature regions in the image. Hence, at the beginning of the proposed system, we have converted each TBI into the corresponding blood perfusion image using BBP model. Then, texture features are extracted from each of the breasts of a patient to measure the asymmetric temperature distribution since it is an indicator of the presence of an abnormality. For our experimental purpose, TBIs of DMR-IR dataset are used. Finally, a feed-forward artificial neural network (FANN) with gradient descent training rule is employed for diagnostic classification of the TBIs into benign and malignant classes. Experimental results reveal that our proposed BBP image model outperforms in classification performance in comparison to the raw grayscale breast thermogram and the existing blood perfusion image model.
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Acknowledgements
Authors are thankful to DBT, Govt. of India for funding a project with Grant no. BT/533/NE/TBP/2014. Sourav Pramanik is also thankful to Department of Electronics and Information Technology (DeitY), Government of India, for providing him PhD Fellowship under Visvesvaraya PhD Scheme.
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Pramanik, S., Banik, D., Bhattacharjee, D., Nasipuri, M., Bhowmik, M.K. (2018). Breast Blood Perfusion (BBP) Model and Its Application in Differentiation of Malignant and Benign Breast. In: Bhattacharyya, S., Gandhi, T., Sharma, K., Dutta, P. (eds) Advanced Computational and Communication Paradigms. Lecture Notes in Electrical Engineering, vol 475. Springer, Singapore. https://doi.org/10.1007/978-981-10-8240-5_45
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DOI: https://doi.org/10.1007/978-981-10-8240-5_45
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